Conditional random fields for Spanish named entity recognition using unsupervised features

Jenny Copara, Jose Ochoa, Camilo Thorne, Goran Glavăs

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Unsupervised features based on word representations such as word embeddings and word collocations have shown to significantly improve supervised NER for English. In this work we investigate whether such unsupervised features can also boost supervised NER in Spanish. To do so, we use word representations and collocations as additional features in a linear chain Conditional Random Field (CRF) classifier. Experimental results (82.44% F-score on the CoNLL-2002 corpus) show that our approach is comparable to some state-of-art Deep Learning approaches for Spanish, in particular when using cross-lingual word representations.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - IBERAMIA 2016 - 15th Ibero-American Conference on AI 2016, Proceedings
EditorsHugo Jair Escalante, Manuel Montes-y-Gomez, Alberto Segura, Juan de Dios Murillo
PublisherSpringer Verlag
Pages175-186
Number of pages12
ISBN (Print)9783319479545
DOIs
StatePublished - 2016
Externally publishedYes
Event15th Ibero-American Conference on Advances in Artificial Intelligence, IBERAMIA 2016 - San Jose, Costa Rica
Duration: Nov 23 2016Nov 25 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10022 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th Ibero-American Conference on Advances in Artificial Intelligence, IBERAMIA 2016
Country/TerritoryCosta Rica
CitySan Jose
Period11/23/1611/25/16

Keywords

  • Collocations
  • Conditional random fields
  • NER for Spanish
  • Word representations

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